Electronic device and method for speech recognition of the same

ABSTRACT

An electronic device for recognizing a user&#39;s speech and a speech recognition method therefor are provided. The electronic device includes a microphone configured to receive a user&#39;s speech, a memory for storing speech recognition models, and at least one processor configured to select a speech recognition model from among the speech recognition models stored in the memory based on an operation state of the electronic device, and recognize the user&#39;s speech received by the microphone based on the selected speech recognition model.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. § 119of a Korean patent application number 10-2019-0096559, filed on Aug. 8,2019 in the Korean Intellectual Property Office, the disclosure of whichis incorporated by reference herein in its entirety.

BACKGROUND 1. Field

The disclosure relates to an electronic device capable of recognizing auser's speech and a speech recognition method thereof

2. Description of the Related Art

Recently, to increase a user's convenience, a speech recognition servicefor recognizing, when a user utters his/her desired command, the user'sspeech and detecting the command included in the user's utterance tothereby provide a service corresponding to the user's intention has beendeveloped and supplied.

Meanwhile, to raise a speech recognition rate and speech recognitionaccuracy, it is important to measure noise of an environment where thespeech recognition service is provided. As noise of the environmentwhere the speech recognition service is provided, noise generated by adevice providing the speech recognition service may have great influenceon speech recognition performance.

The above information is presented as background information only toassist with an understanding of the disclosure. No determination hasbeen made, and no assertion is made, as to whether any of the abovemight be applicable as prior art with regard to the disclosure.

SUMMARY

Aspects of the disclosure are to address at least the above-mentionedproblems and/or disadvantages and to provide at least the advantagesdescribed below. Accordingly, an aspect of the disclosure to provide anelectronic device capable of recognizing a user's speech and a speechrecognition method thereof.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented disclosure.

In accordance with an aspect of the disclosure, an electronic device isprovided. The electronic device includes a microphone configured toreceive a user's speech, a memory storing a plurality of speechrecognition models, and at least one processor configured to select aspeech recognition model from among the plurality of speech recognitionmodels stored in the memory based on an operation state of theelectronic device, and recognize the user's speech received by themicrophone based on the selected speech recognition model.

The operation state may include at least one of an operation mode or anoperation intensity of the electronic device.

The at least one processor may be further configured to select a speechrecognition model corresponding to a state condition fulfilling theoperation state from among a plurality of state conditions respectivelymatching the plurality of speech recognition models, as a speechrecognition model corresponding to the operation state.

The at least one processor may be further configured to change, when theoperation state changes, the selected speech recognition model based onthe changed operation state, and recognize the user's speech based onthe changed speech recognition model.

The at least one processor may be further configured to determine asensitivity of the selected speech recognition model based on theoperation state, and recognize the user's speech based on the selectedsensitivity.

The at least one processor may be further configured to change, when theoperation state changes, the sensitivity based on the changed operationstate.

The at least one processor may be further configured to determine theuser's intention based on a result of speech recognition on the user'sspeech, and change the sensitivity based on the user's intention.

The at least one processor may be further configured to determine afirst pattern for noise of a surrounding environment based on theoperation state, and generate a speech recognition model based on thedetermined first pattern.

The at least one processor may be further configured to determine asecond pattern for noise of the electronic device based on the operationstate, and determine the first pattern for the noise of the surroundingenvironment based on the second pattern.

The plurality of speech recognition models may include a plurality ofspeech recognition models for recognizing a wakeup word.

The electronic device may include at least one of a cleaner, an airconditioner, a refrigerator, a washing machine, or a clothes careapparatus.

In accordance with an aspect of the disclosure, an electronic device isprovided. The electronic device includes a microphone configured toreceive a user's speech; a transceiver configured to communicate with aserver, and at least one processor configured to select a speechrecognition model from among a plurality of speech recognition modelsreceived from the server based on an operation state of the electronicdevice, and recognize the user's speech received by the microphone basedon the selected speech recognition model.

In accordance with another aspect of the disclosure, a speechrecognition method of an electronic device is provided. The methodincludes receiving a user's speech, selecting a speech recognition modelfrom among a plurality of speech recognition models stored in advance,based on an operation state of the electronic device, and recognizingthe user's speech based on the selected speech recognition model.

The selecting of the speech recognition model may include selecting aspeech recognition model corresponding to a state condition fulfillingthe operation state from among a plurality of state conditionsrespectively matching the plurality of speech recognition models, as aspeech recognition model corresponding to the operation state.

The speech recognition method may further include changing, when theoperation state changes, the selected speech recognition model based onthe changed operation state, wherein the recognizing of the user'sspeech may include recognizing the user's speech based on the changedspeech recognition model.

The speech recognition method may further include determining asensitivity of the selected speech recognition model based on theoperation state, wherein the recognizing of the user's speech based onthe selected speech recognition model may include recognizing the user'sspeech based on the determined sensitivity.

The speech recognition method may further include changing, when theoperation state changes, the sensitivity of the selected speechrecognition model based on the changed operation state, wherein therecognizing of the user's speech based on the selected speechrecognition model may include recognizing the user's speech based on thechanged sensitivity.

The speech recognition method may further include determining a user'sintention based on a result of speech recognition on the user's speech,and changing the sensitivity based on the user's intention.

The speech recognition method may further include determining a firstpattern for noise of a surrounding environment based on the operationstate, and generating a speech recognition model based on the determinedfirst pattern.

Other aspects, advantages, and salient features of the disclosure willbecome apparent to those skilled in the art from the following detaileddescription, which, taken in conjunction with the annexed drawings,discloses various embodiments of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the disclosure will be more apparent from the followingdescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 shows a speech recognition system according to an embodiment ofthe disclosure;

FIG. 2 is a control block diagram of an electronic device according to afirst embodiment of the disclosure;

FIG. 3 shows an example of a state condition table for selecting aspeech recognition model in the electronic device according to the firstembodiment of the disclosure;

FIG. 4 is a control block diagram of an electronic device according to asecond embodiment of the disclosure;

FIG. 5 shows an outer appearance of a robot cleaner as an implementationexample of the electronic device according to the second embodiment ofthe disclosure;

FIG. 6 shows a bottom of the robot cleaner as the implementation exampleof the electronic device according to the second embodiment of thedisclosure;

FIG. 7 shows an example of a state condition table for selecting aspeech recognition model in the electronic device according to thesecond embodiment of the disclosure;

FIG. 8 is a control block diagram of an electronic device according to athird embodiment of the disclosure;

FIG. 9 shows an outer appearance of an air conditioner as animplementation example of the electronic device according to the thirdembodiment of the disclosure;

FIG. 10 is an exploded perspective view of the air conditioner as theimplementation example of the electronic device according to the thirdembodiment of the disclosure; and

FIGS. 11, 12 and 13 are flowcharts showing speech recognition methods ofan electronic device according to various embodiments of the disclosure.

Throughout the drawings, like reference numerals will be understood torefer to like parts, components, and structures.

DETAILED DESCRIPTION

The following description with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of variousembodiments of the disclosure as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the various embodiments describedherein can be made without departing from the scope and spirit of thedisclosure. In addition, descriptions of well-known functions andconstructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used by theinventor to enable a clear and consistent understanding of thedisclosure. Accordingly, it should be apparent to those skilled in theart that the following description of various embodiments of thedisclosure is provided for illustration purpose only and not for thepurpose of limiting the disclosure as defined by the appended claims andtheir equivalents.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.Thus, for example, reference to “a component surface” includes referenceto one or more of such surfaces.

Hereinafter, like reference numerals will refer to like componentsthroughout this specification. This specification does not describe allcomponents of the embodiments, and general information in the technicalfield to which the disclosure belongs or overlapping information betweenthe embodiments will not be described. As used herein, the terms“portion”, “part, “module, “member” or “block” may be implemented assoftware or hardware, and according to embodiments, a plurality of“portions”, “parts, “modules, “members” or “blocks” may be implementedas a single component, or a single “portion”, “part, “module, “member”or “block” may include a plurality of components.

Also, it will be understood that when a certain part “includes” acertain component, the part does not exclude another component but canfurther include another component, unless the context clearly dictatesotherwise.

Also, it will be understood that when the terms “includes,” “comprises,”“including,” and/or “comprising,” when used in this specification,specify the presence of a stated component, but do not preclude thepresence or addition of one or more other components.

In the entire specification, it will also be understood that when acertain element is referred to as being “on” or “over” another element,it can be directly on the other element or intervening elements may alsobe present.

Also, it will be understood that, although the terms first, second, etc.may be used herein to describe various components, these componentsshould not be limited by these terms. These terms are only used todistinguish one component from another.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.

Reference numerals used in operations are provided for convenience ofdescription, without describing the order of the operations, and theoperations can be executed in a different order from the stated orderunless a specific order is definitely specified in the context.

Throughout the disclosure, the expression “at least one of a, b or c”indicates only a, only b, only c, both a and b, both a and c, both b andc, all of a, b, and c, or variations thereof.

Hereinafter, an operation principle and embodiments of the disclosurewill be described with reference to the accompanying drawings.

An electronic device according to an aspect may be a device capable ofreceiving sound through a microphone and transmitting and receiving datathrough communications with an external device. For example, theelectronic device may be a home appliance, such as a robot cleaner, anair conditioner, a refrigerator, a washing machine, a clothes careapparatus, an air cleaner, a humidifier, an oven, a microwave oven, anaudio system, a television (TV), a speaker, a computer, etc., or amobile device, such as a smart phone, a tablet personal computer (PC), aPC, digital assistant (PDA), etc.

Also, the electronic device according to an aspect may perform speechrecognition or provide a service provided through speech recognition,and perform machine learning or provide results of machine learningaccording to some embodiments. Accordingly, a cleaner, an airconditioner, a refrigerator, a TV, a speaker, etc., which is theelectronic device, may also be respectively referred to as an artificialintelligence (AI) cleaner, an AI air conditioner, an AI refrigerator, anAI TV, an AI speaker, etc.

The electronic device according to an aspect may perform operationswhich will be described below, and have no limitation regarding itstype, implementation method, name, etc.

FIG. 1 shows a speech recognition system according to an embodiment ofthe disclosure.

Referring to FIG. 1, a user of a speech recognition system 1 accordingto an embodiment of the disclosure may utter a control command for anelectronic device 100 to input the control command to the electronicdevice 100.

The electronic device 100 may recognize the user's speech and output aresponse corresponding to the user's speech.

After the electronic device 100 recognizes the user's speech, theelectronic device 100 may output a response corresponding to the user'sintention as a response corresponding to the user's speech. At thistime, the electronic device 100 may generate a control command or aconversational response corresponding to the user's intention, andoutput the control command or the conversational response.

Meanwhile, the electronic device 100 may be implemented as a homeappliance providing various functions, such as a robot cleaner 100 a, anair conditioner 100 b, a refrigerator 100 c, a washing machine 100 d, anoven 100 e, a clothes care apparatus 100 f, etc.

An operation state of the electronic device 100 may be a factorinfluencing speech recognition performance for a user's speech. Thereason may be because noise of the electronic device 100 generatedaccording to an operation state of the electronic device 100 may act asa factor interfering with accurate speech recognition.

Also, as the electronic device 100 provides various functions, theoperation state of the electronic device 100 may change in every minute.When the operation state of the electronic device 100 changes, noisegenerated in the electronic device 100 may also change. Accordingly, toincrease the accuracy of speech recognition, a speech recognition modelthat responds appropriately to a change of environmental noise includingnoise generated in the electronic device 100 may need to be used.

FIG. 2 is a control block diagram of an electronic device according to afirst embodiment of the disclosure, and FIG. 3 shows an example of astate condition table for selecting a speech recognition model in theelectronic device according to the first embodiment of the disclosure.

Referring to FIGS. 2 and 3, the electronic device 100 according to thefirst embodiment of the disclosure may include a microphone 110 forreceiving a user's speech, a communicator or transceiver 120, aprocessor 130, a memory 140, and a driver 150.

The microphone 110 may receive sound and convert the sound into anelectrical signal. A single microphone 110 may be provided in theelectronic device 100, or a plurality of microphones 110 may be providedto increase speech recognition performance.

The microphone 110 may be mounted on an outer surface of the electronicdevice 100 or may be physically separated from the electronic device 100to be positioned close to a user. For example, the microphone 110 may beimplemented as a movable stand microphone, a wearable microphone such asa headset, etc. However, an installation position or implementation typeof the microphone 110 is not limited as long as the microphone 110receives a user's speech.

A speech uttered by a user may be converted into an electrical signalthrough the microphone 110 and input to the processor 130. Hereinafter,a speech converted into an electrical signal is also referred to as aspeech signal.

The transceiver 120 may include a communication circuit for dataexchange with an external device or for data exchange between componentsincluded in the electronic device 100.

The transceiver 120 may transmit/receive various information to/from aserver (not shown). The transceiver 120 may receive a speech recognitionmodel from the server (not shown) and store the received speechrecognition model in the memory 140.

For this, the transceiver 120 may include at least one communicationmodule for transmitting/receiving data according to a predefinedcommunication standard. For example, the transceiver 120 may include atleast one of a short-range communication module, a wired communicationmodule, or a wireless communication module.

The short-range communication module may include various short-rangecommunication modules, such as a Bluetooth module, an Infraredcommunication module, a Radio Frequency Identification (RFID)communication module, a Wireless Local Access Network (WLAN)communication module, a Near Field Communication (NFC) module, a Zigbeecommunication module, etc., to transmit/receive signals through awireless communication network at a short distance.

The wired communication module may include various cable communicationmodules, such as a Universal Serial Bus (USB), a High DefinitionMultimedia Interface (HDMI), a Digital Visual Interface (DVI),Recommended Standard-232 (RS-232), power line communication, or a PlainOld Telephone Service (POTS), as well as various wired communicationmodules, such as a Controller Area Network (CAN) communication module, aLocal Area Network (LAN) module, a Wide Area Network (WAN) module, or aValue Added Network (VAN) module.

The wireless communication module may include wireless communicationmodules supporting various wireless communication methods, such as aglobal System for Mobile Communication (GSM), Code Division MultipleAccess (CDMA), Wideband Code Division Multiple Access (WCDMA), UniversalMobile Telecommunications System (UMTS), Time Division Multiple Access(TDMA), Long Term Evolution (LTE), etc., as well as a Wireless Fidelity(WiFi) module and a Wireless broadband module.

The wireless communication module may include a wireless communicationinterface including an antenna and a transmitter for transmittingsignals. Also, the wireless communication module may further include acall conversion module for converting a digital control signal outputfrom a processor through the wireless communication interface into ananalog wireless signal, according to a control of a controller.

The wireless communication module may include a wireless communicationinterface including an antenna and a receiver for receiving signals.Also, the wireless communication module may further include a signalconversion module for demodulating an analog wireless signal receivedthrough the wireless communication interface into a digital controlsignal.

The memory 140 may store various data related to operations of theelectronic device 100, and store data related to speech recognition.

The memory 140 may store a plurality of speech recognition models 141(141-1, 141-2, . . . 141-n). The memory 140 may store the plurality ofspeech recognition models 141 respectively corresponding to variousoperation states of the electronic device 100.

The speech recognition models 141 may be received from an externalserver (not shown) through the transceiver 120, although not limitedthereto. However, the speech recognition models 141 may have been storedin advance in the memory 140 upon design.

For this, the memory 140 may be implemented as at least one of anon-volatile memory device (for example, a cache, Read Only Memory(ROM), Programmable ROM (PROM), Erasable Programmable ROM (EPROM),Electrically Erasable Programmable ROM (EEPROM), and Flash memory), avolatile memory device such as Random Access Memory (RAM), or a storagemedium, such as Hard Disk Drive (HDD) and Compact Disk-Read Only Memory(CD-ROM), although not limited thereto. The memory 140 may beimplemented as a chip that is separated from the processor 130 whichwill be described later, or the memory 140 and the processor 130 may beintegrated into a single chip.

The driver 150 may include at least one component for generating adriving force for enabling the electronic device 100 to provide at leastone function or transferring the driving force.

The driver 150 may include various components according toimplementation examples of the electronic device 100. For example, thedriver 150 may include a motor, and further include a fan according tosome cases. The driver 150 will be described in detail, later.

The electronic device 100 may include the processor 130 electricallyconnected to the microphone 110, the transceiver 120, the memory 140,and the driver 150.

The processor 130 may recognize a user's speech received through themicrophone 110.

More specifically, the processor 130 may output an utterance in a textform based on a speech signal transferred from the microphone 110 torecognize a user's speech as a sentence. For this, the processor 130 mayinclude a speech recognition engine.

For example, the processor 130 may detect an actual speech sectionincluded in an input speech through End Point Detection (EPD), andextract a feature vector of the input speech from the detected actualspeech section. At this time, the processor 130 may apply feature vectorextraction technology, such as Cepstrum, Linear Predictive Coefficient(LPC), Mel Frequency Cepstral Coefficient (MFCC), or Filter Bank Energy,to the detected actual speech section to extract the feature vector ofthe input speech.

The processor 130 may compare the extracted feature vector to a trainedreference pattern to obtain a result of recognition. For this, theprocessor 130 may use the speech recognition models 141 stored in thememory 140.

The speech recognition models 141 may include at least ones of acousticmodels for modeling and comparing signal characteristics of a speech orlanguage models for modeling a linguistic order relation of words,syllables, etc. corresponding to a recognition vocabulary.Alternatively, the speech recognition models 141 may be models intowhich acoustic models and language models are integrated.

The acoustic models may be divided into a direct comparison method ofsetting a recognition target to a feature vector model and comparing thefeature vector model to a feature vector of speech data, and astatistical model method of statistically processing and using a featurevector of a recognition target.

The direct comparison method is a method of setting a unit, such as aword, a phoneme, etc., being a recognition target to a feature vectormodel and determining similarity between an input speech and the featurevector model. A representative example of the direct comparison methodis a vector quantization method. The vector quantization method is amethod of mapping feature vectors of input speech data to a codebookwhich is a reference model to encode to representative values andcomparing the encoded values to each other.

The statistical model method is a method of configuring a unit for arecognition target into a state sequence and using a relation betweenstate sequences. The state sequence may be configured with a pluralityof nodes. Methods of using a relation between state sequences mayinclude Dynamic Time Warping (DTW), Hidden Markov Model (HMM), a methodusing Artificial Neural Network (ANN), etc.

The Dynamic Time Warping (DTW) is a method of compensating fordifferences on the time axis upon comparison to a reference model inconsideration of dynamic characteristics of speeches that signal lengthschange over time even when the same person pronounces the same word, andthe Hidden Markov Model (HMM) is recognition technology of estimating astate change probability and an observation probability of a nodethrough learning data after assuming that a speech is a Markov processhaving a state change probability and an observation probability of anode (output symbol) in each state, and calculating a probability thatan input speech will be generated in an estimated model.

Meanwhile, the language model for modeling a linguistic order relationof words, syllables, etc. reduces acoustic ambiguity and recognitionerrors by applying an order relation between units constituting alanguage to units obtained from speech recognition. As the languagemodel, there are a statistical language model and a model based onFinite State Automata (FSA), and the statistical language model uses achain probability of words, such as Unigram, Bigram, Trigram, etc.

The speech recognition model uses any method of the above-describedmethods for recognizing a speech. For example, the processor 130 may usea speech recognition model including an acoustic model to which a HiddenMarkov Model (HMM) is applied, or a speech recognition model using aN-best search method into which an acoustic model and a language modelare integrated.

Also, the processor 130 may calculate a confidence value to securereliability of a recognition result. A confidence value is a measurerepresenting how reliable a speech recognition result is. For example, aconfidence value of a phoneme or word as a recognized result may bedefined as a relative value for a probability that the correspondingphoneme or word has been uttered from different phonemes or words.Accordingly, a confidence value may be a value ranging from 0 to 1 or avalue ranging from 0 to 100. When a confidence value of a recognitionresult exceeds a predefined threshold value, the recognition result maybe output to perform an operation corresponding to the recognitionresult, and, when a confidence value of a recognition result is smallerthan or equal to the predefined threshold value, the recognition resultmay be rejected.

Meanwhile, the processor 130 may recognize a predefined word. In thiscase, the processor 130 may determine whether a recognition resultmatches the predefined word. When a rate of matching between therecognition result and the predefined word is greater than or equal to apredefined reference value, the processor 130 may determine that thecorresponding word has been uttered by a user.

For example, the processor 130 may recognize a predefined wakeup word.The wakeup word may be a control command for activating a speechrecognition mode.

The processor 130 may recognize a wakeup word by using a pre-storedspeech recognition model. In this case, the pre-stored speechrecognition model may have a recognition range for recognizing apredefined word. For example, the speech recognition model may be aspeech recognition model for recognizing a wakeup word.

When a recognition range is limited to a predefined wakeup word, acapacity of the memory 140 or the processor 130, required for speechrecognition, may be reduced, although not limited thereto. However, theprocessor 130 may recognize more languages.

Also, the processor 130 may apply Natural Language Understanding (NLU)to an utterance in a text form as a recognition result to understand auser's intention included in the utterance.

The processor 130 may perform morpheme analysis on an utterance in atext form and analyze a speech-act that the utterance has. Speech-actanalysis may be a task of analyzing a user's utterance intention tounderstand utterance intention about whether the user asks a question,whether the user makes a request, whether the user gives an answer,whether the user simply expresses emotion, etc.

The processor 130 may output a control command or a responsecorresponding to the user's intention understood through the morphemeanalysis and speech-act analysis. The processor 130 may generate acontrol command for executing a service corresponding to the user'sintention or output a response corresponding to the user's intention.

Meanwhile, a speech recognition model for performing an entity or a partof the above-described speech recognition process may be stored in thememory 140, and a plurality of different speech recognition models maybe stored in the memory 140. The plurality of speech recognition modelsstored in the memory 140 may have different parameters. That is, theplurality of speech recognition models may be models modeled to performthe above-described speech recognition process by using differentparameters, or models using different pieces of reference data.

The plurality of different speech recognition models may be modeled tobe suitable to different environments and stored in the memory 140.Accordingly, the different speech recognition models may be appliedaccording to environments using speech recognition to achieve a highspeech recognition rate and high speech recognition accuracy. Therefore,the processor 130 may select a speech recognition model that is suitableto an environment where speech recognition is performed. Hereinafter, anoperation of selecting a speech recognition model in the processor 130will be described in detail.

The processor 130 may select a speech recognition model from among theplurality of speech recognition models stored in the memory 140, andrecognize a user's speech by using the selected speech recognitionmodel. At this time, the processor 130 may reflect a state of theelectronic device 100, which may influence speech recognition, to selecta speech recognition model from among the plurality of speechrecognition models stored in advance.

More specifically, the processor 130 may select a speech recognitionmodel from among the plurality of speech recognition models stored inadvance, based on an operation state of the electronic device 100.Herein, the operation state of the electronic device 100 may be a statefactor that may influence noise generated in the electronic device 100,that is, a state factor from which noise of the electronic device 100may be estimated.

The operation state of the electronic device 100 may include at leastone of an operation mode or an operation intensity of the electronicdevice 100.

The operation mode may be information representing a kind of a functionprovided by the electronic device 100. For example, the operation modemay be classified into a standby mode representing a state in which theelectronic device 100 waits for receiving a command from a user, or anexecution mode representing a state in which the electronic device 100operates in response to an operation command received from a user. Also,the operation mode may be classified into at least one mode representingkinds of various functions provided by the electronic device 100. Avolume, intensity, pattern, etc. of noise generated in the electronicdevice 100 may depend on the operation mode.

Meanwhile, an operation mode may be set automatically according to acontrol command of the processor 130, or received from a user. When anoperation mode is received from a user, the electronic device 100 mayfurther include an input device (not shown).

An operation intensity may be information representing an intensity of adriving force generated in the electronic device 100. For example, theoperation intensity may be expressed as data representing an intensityof a motor, such as torque, revolution per minute (rpm), current, etc.of the motor. Also, the operation intensity may be expressed as datarepresenting rpm of a fan. A volume, intensity, pattern, etc. of noisegenerated in the electronic device 100 may depend on the operationintensity.

In addition to the operation mode and the operation intensity, theoperation state may further include at least one of an operatingcomponent or an operation location.

The operating component may represent a kind of a component being in anoperation state among at least one component included in the electronicdevice 100. For example, the operating component may represent acomponent being in an operation state among at least one componentincluded in the driver 150. Different components may generate noisehaving different volumes, intensities, patterns, etc. when operating.

The operation location may be a location of the electronic device 100,and be a location that may influence noise generated in the electronicdevice 100. The operation location may be classified according topredefined areas. For example, the operation location may be classifiedinto under the bed, near the wall, carpet, etc. A waveform of noisegenerated upon operation may depend on the operation location, andaccordingly, a volume, intensity, pattern, etc. of noise may changeaccording to the operation location.

Based on the operation state described above, the processor 130 mayselect a speech recognition model corresponding to the operation statefrom among the plurality of speech recognition models. That is, theprocessor 130 may select a speech recognition model capable of securinga high speech recognition rate and high recognition performanceaccording to the operation state of the electronic device 100.

More specifically, the processor 130 may identify a state conditionfulfilling the operation state from among predefined state conditionsfor the plurality of speech recognition models, and select a speechrecognition model corresponding to the state condition fulfilling theoperation state, as a speech recognition model corresponding to theoperation state. The predefined state conditions for the plurality ofspeech recognition models may be conditions for operation states, andmay be defined upon design or according to a user's input.

For this, a state condition table in which state conditions foroperation states match speech recognition models may be stored in thememory 140. The processor 130 may compare the operation state of theelectronic device 100 to the state condition table to determine a speechrecognition model corresponding to the operation state.

For example, as shown in FIG. 3, the state conditions may be defined inadvance as conditions for operation modes among operation states, and astate condition table in which speech recognition models match operationmodes may be stored in the memory 140.

In this case, a first speech recognition model, a second speechrecognition model, and a third speech recognition model stored in thememory 140 may be models modeled to have different speech recognitionparameters.

When an operation mode of the electronic device 100 is a first mode, theprocessor 130 may select the first speech recognition model from amongthe plurality of speech recognition models. Likewise, according to theexample of FIG. 3, when an operation mode of the electronic device 100is a second mode, the processor 130 may select the second speechrecognition model, and, when an operation mode of the electronic device100 is a third mode, the processor 130 may select the third speechrecognition model.

As such, by selecting a speech recognition model that is suitable to anoise environment according to an operation state of the electronicdevice 100 instead of unconditionally using a speech recognition modelmodeled in a normal environment, the processor 130 may raise a speechrecognition rate and speech recognition accuracy.

Particularly, when a speech recognition model is a wakeup wordrecognition model, a wakeup word may be accurately recognized despitenoise of the electronic device 100. Accordingly, the wakeup word may berecognized quickly and accurately although the user does not again utterthe wakeup word.

Meanwhile, when the operation state of the electronic device 100changes, noise generated in the electronic device 100 may also change.

When the operation state of the electronic device 100 changes, theprocessor 130 may change the selected speech recognition model based onthe changed operation state, and recognize a user's speech based on thechanged speech recognition model. For this, the processor 130 mayacquire an operation state in real time or at predefined time intervals.

For example, according to the example of FIG. 3, when the first modechanges to the second mode, the processor 130 may change the firstspeech recognition mode to the second speech recognition mode.Thereafter, when the second mode changes to the third mode, theprocessor 130 may change the second speech recognition model to thethird speech recognition model, and perform speech recognition by usingthe third speech recognition model.

By changing a speech recognition model according to an operation stateof the electronic device 100, the processor 130 may increase accuracyand efficiency of speech recognition.

The processor 130 may determine a parameter of a speech recognitionmodel based on an operation state, and perform speech recognition basedon the determined parameter. The parameter of the speech recognitionmodel may include a sensitivity, and may include various speechrecognition-related parameters in addition to a sensitivity.Hereinafter, as an example of a parameter of a speech recognition model,a sensitivity will be described.

The processor 130 may identify a state condition fulfilling an operationstate from among predefined conditions for operation states forsensitivities, and determine a sensitivity corresponding to the statecondition fulfilling the operation state as a sensitivity of a speechrecognition model.

For this, a sensitivity table in which sensitivities of speechrecognition models match operation states may be stored in the memory140. The processor 130 may compare an operation state of the electronicdevice 100 to the sensitivity table to determine a sensitivity of aspeech recognition model corresponding to the operation state.

Sensitivities may be classified into a plurality of predefined levelsthat are different according to the speech recognition models. Theprocessor 130 may determine a sensitivity level corresponding to anoperation state.

For example, when the electronic device 100 is in the first mode, theprocessor 130 may determine a sensitivity of a selected speechrecognition model to be a first level. When the electronic device 100 isin the second mode in which a greater volume of noise is generated thanthat of the first mode, the processor 130 may determine a sensitivity ofa speech recognition model to be a second level. Herein, the secondlevel may have a higher sensitivity than the first level.

In this way, because the processor 130 determines a sensitivity of aspeech recognition model according to an operation state, the processor130 may reduce influence by noise of the electronic device 100.Accordingly, accuracy and efficiency of speech recognition may increase.

Meanwhile, when the operation state of the electronic device 100changes, noise generated in the electronic device 100 may also change.Accordingly, a case in which a sensitivity of a speech recognition modelneeds to change may occur.

When an operation state changes, the processor 130 may change asensitivity based on the changed operation state, and recognize a user'sspeech based on the changed sensitivity. For this, the processor 130 mayacquire an operation state in real time or at predefined time intervals.

Operation of changing a sensitivity of a speech recognition model may beperformed simultaneously with operation of changing a speech recognitionmodel. That is, when an operation state changes, the processor 130 maychange a speech recognition model and a sensitivity of the speechrecognition model based on the changed operation state, although notlimited thereto. However, the processor 130 may change a sensitivity ofa speech recognition mode, without changing the speech recognitionmodel.

Meanwhile, a sensitivity of a speech recognition model may changeaccording to a user's intention, instead of an operation state.

The processor 130 may determine a user's intention based on a result ofspeech recognition on the user's speech, and change a sensitivity basedon the user's intention.

More specifically, the processor 130 may understand context based on aresult of speech recognition, understand a user's intention aboutwhether the user will continue to utter or finish uttering according tothe context, and change a sensitivity based on the user's intention.

For example, when the processor 130 determines that a user will continueto utter based on a result of speech recognition on the user's speech,the processor 130 may change a sensitivity to a higher sensitivity. Whenthe processor 130 determines that a user will finish uttering based on aresult of speech recognition on the user's speech, the processor 130 maychange a sensitivity to a lower sensitivity.

In this way, because a sensitivity of a speech recognition model changesaccording to a user's intention, an accurate speech recognition servicemay be provided in various environments where speech recognition isused.

Particularly, upon wakeup word recognition, because a sensitivitychanges according to a user's intention after the user utters the wakeupword, more accurate speech recognition may be possible. Accordingly,user convenience may increase.

Meanwhile, the processor 130 may generate a speech recognition model fornoise of a surrounding environment based on an operation state, andstore the speech recognition model in the memory 140 or the server (notshown).

The processor 130 may determine a first pattern for noise of asurrounding environment based on an operation state, and generate aspeech recognition model based on the first pattern. The noise of thesurrounding environment may be noise excluding noise of the electronicdevice 200 among noise input to the microphone 110.

More specifically, the processor 130 may determine a second pattern fornoise of the electronic device 100, that is, noise generated in theelectronic device 100, based on an operation state, and determine afirst pattern for noise of a surrounding environment based on the secondpattern.

For this, noise patterns according to operation states may be stored inthe memory 140 or the server (not shown). The processor 130 maydetermine a noise pattern corresponding to an operation state amongnoise patterns stored in the memory 140 or the server (not shown) to bea second pattern for noise of the electronic device 100.

The processor 130 may determine noise excluding noise of the electronicdevice 100 of the second pattern among noise input to the microphone 110to be noise of a surrounding environment, and extract a first patternfor the noise of the surrounding environment based on signalcharacteristics of the determined noise of the surrounding environment.

The processor 130 may generate a speech recognition model based on thefirst pattern for the noise of the surrounding environment.

Also, the processor 130 may change at least one of a pre-selected speechrecognition model or a sensitivity of the speech recognition model basedon the first pattern for the noise of the surrounding environment.

Thereby, accurate and efficient speech recognition may be possibledespite noise of a surrounding environment as well as noise generated inthe electronic device. Accordingly, user convenience may increase.

Meanwhile, data for an algorithm for controlling operations ofcomponents in the electronic device 100 or a program for embodying thealgorithm may be stored in the memory 140. The processor 130 may performthe above-described operations by using the data stored in the memory140. The memory 140 and the processor 130 may be implemented as separatechips or integrated into a single chip.

At least one component may be added or omitted to correspond toperformance of the components of the electronic device 100 shown in FIG.2. Also, it will be easily understood by one of ordinary skill in theart that relative positions of the components may change to correspondto the performance or structure of the system.

Meanwhile, the components shown in FIG. 2 may be software componentsand/or hardware components, such as a Field Programmable Gate Array(FPGA) and an Application Specific Integrated Circuit (ASIC).

Hereinafter, an implementation example of the electronic device 100 willbe described with reference to FIGS. 4 to 7.

FIG. 4 is a control block diagram of an electronic device according to asecond embodiment of the disclosure, FIG. 5 shows an outer appearance ofa robot cleaner as an implementation example of the electronic deviceaccording to the second embodiment of the disclosure, FIG. 6 shows abottom of the robot cleaner as the implementation example of theelectronic device according to the second embodiment of the disclosure,and FIG. 7 shows an example of a state condition table for selecting aspeech recognition model in the electronic device according to thesecond embodiment of the disclosure.

Referring to FIGS. 4, 5, and 6, the electronic device 100 according tothe second embodiment of the disclosure may be implemented as a robotcleaner including a main body 101, and a bumper 102 positioned in afront side of the main body 101.

As shown in FIG. 4, the electronic device 100 according to the secondembodiment of the disclosure may include the microphone 110, thetransceiver 120, the processor 130, the memory 140, and the driver 150.

The microphone 110, the transceiver 120, the processor 130, and thememory 140 have been described above with reference to FIG. 2, and thedriver 150 may include an internal component of the robot cleaner.Hereinafter, features of the second embodiment, which are different fromthose of the first embodiment, will be described.

The main body 101 of the electronic device 100 implemented as the robotcleaner according to the second embodiment of the disclosure may besubstantially in a shape of a cylinder, as shown in FIG. 5. Morespecifically, the main body 101 may include a top surface 101 a formedsubstantially in a shape of a circle, and a side surface 101 b formedalong an edge of the top surface 101 a.

A plurality of microphones 110 a and 110 b may be positioned on the topsurface 101 a, although not limited thereto. However, the microphone 110may be positioned on at least one of the bumper 102, a front surface 101c, the side surface 101 b, or a bottom (see FIG. 6).

The bumper 102 may reduce an impact transferred to the main body 101upon a collision with an obstacle, and sense a collision with anobstacle. The obstacle may be an object, a person, or an animal thatinterferes with traveling of the electronic device 100. The obstacle mayinclude a wall partitioning a cleaning space, furniture positioned in acleaning space, and a person or animal located in a cleaning space.

When the electronic device 100 travels, the bumper 102 may face in afront direction, and a direction in which the bumper 102 faces may bedefined as a ‘front’ direction of the electronic device 100, as shown inFIG. 5.

Inside and outside the main body 101 of the electronic device 100,components for performing functions (moving and cleaning) of the robotcleaner may be provided, and the components may be included in thedriver 150.

As shown in FIG. 4, the driver 150 may include a motor 151, a fan 152, abrush 153, and a wheel 154.

The motor 151 may generate a driving force, and transfer the generateddriving force to at least one of the fan 152, the brush 153, or thewheel 154. For this, a plurality of motors 151 may be provided togenerate and transfer driving forces independently for the fan 152, thebrush 153, and the wheel 154, respectively.

The wheel 154 may include a first wheel 154 a positioned on a left sideof the bottom of the electronic device 100 and a second wheel 154 bpositioned on a right side of the bottom of the electronic device 100,and rotate by the motor 151 to move the main body 101.

When the main body 101 moves, a rotation shaft of a roller 145 mayrotate, and accordingly, the roller 145 may support the main body 101without interfering with traveling of the electronic device 100.

Also, the electronic device 100 may include a driving circuit forsupplying driving current to the motor 151, and a rotation sensor (forexample, an encoder, a hall sensor, etc.) for sensing rotations of thewheel 154.

The brush 153 may be positioned in a dust suction port 101 e formed inthe bottom of the main body 101, as shown in FIG. 6. The brush 153 mayrotate on a rotation shaft positioned to be horizontal to a floor of acleaning space to scatter dust gathered on the floor to inside of thedust suction port 101 e.

Also, the electronic device 100 may include various sensors foracquiring at least one of an operation mode, an operation intensity, anoperating component, or an operation location.

As such various components operate in different operation states,characteristics of noise generated in the electronic device 100 may alsochange accordingly.

Accordingly, the processor 130 may select a speech recognition modelcapable of securing a high recognition rate and high recognitionperformance according to an operation state of the electronic device100, and perform speech recognition based on the selected speechrecognition model. This operation has been described above in the firstembodiment, and state conditions for operation states may be modifiedappropriately according to an implementation example of the electronicdevice 100.

Referring to FIG. 7, state conditions may have been defined in advanceas conditions for an operation mode, an operation intensity, anoperating component, and an operation location among operation states,and a state condition table in which speech recognition models matchoperation modes may be stored in the memory 140.

A first speech recognition model, a second speech recognition model, anda third speech recognition model stored in the memory 140 may be modelsmodeled to have different speech recognition parameters.

The operation mode may be classified according to functions of the robotcleaner, and the operation intensity may be represented as at least onevalue of rpm of the motor 151, torque of the motor 151, a magnitude ofdriving current provided to the motor 151, strength of the fan 152, orrpm of the fan 152.

When an operation mode of the electronic device 100 is the first mode,rpm of the motor 151 is smaller than or equal to a first threshold valueX1, and an operating component is the motor 151, the processor 130 mayselect the first speech recognition model from among the plurality ofspeech recognition models.

When an operation mode of the electronic device 100 is the second mode,rpm of the motor 151 is greater than the first threshold value X1 andsmaller than or equal to a second threshold value X2, operatingcomponents are the motor 151 and the fan 152, and an operation locationis near the wall, the processor 130 may select the second speechrecognition model.

When an operation mode of the electronic device 100 is the third mode,rpm of the motor 151 is greater than a third threshold value X3, andoperating components are the motor 151, the fan 152 and the brush 153,and an operation location is under the bed, the processor 130 may selectthe third speech recognition model.

As such, by selecting a speech recognition model that is suitable to anoise environment according to a detailed operation state of theelectronic device 100 instead of unconditionally using a speechrecognition model modeled in a normal environment, the processor 130 mayraise a speech recognition rate and speech recognition accuracy.

Particularly, when a speech recognition model is a wakeup wordrecognition model, a wakeup word may be accurately recognized despitenoise of the electronic device 100. Accordingly, the wakeup word may berecognized quickly and accurately although the user does not again utterthe wakeup word.

Like the first embodiment of the disclosure, the processor 130 accordingto the second embodiment of the disclosure may determine a sensitivityof a speech recognition model according to an operation state includingat least one of an operation mode, an operation intensity, an operatingcomponent, or an operation location, and change at least one of thespeech recognition model or the sensitivity of the speech recognitionmodel according to a change of the operation state. Also, the processor130 may change the sensitivity based on a user's intention or generate aspeech recognition model for noise of a surrounding environment based onan operation state. This operation has been described above in the firstembodiment, and therefore, further descriptions thereof will be omitted.

Hereinafter, another implementation example of the electronic device 100will be described with reference to FIGS. 8 to 10.

FIG. 8 is a control block diagram of an electronic device according to athird embodiment of the disclosure. FIG. 9 shows an outer appearance ofan air conditioner as an implementation example of the electronic deviceaccording to the third embodiment of the disclosure, and FIG. 10 is anexploded perspective view of the air conditioner as the implementationexample of the electronic device according to the third embodiment ofthe disclosure.

Referring to FIGS. 8, 9, and 10, the electronic device 100 according tothe third embodiment of the disclosure may be implemented as an airconditioner including a housing 10 having at least one outlet opening 41(41 a, 41 b, 41 c), a heat exchanger 20 for exchanging heat with airentered inside of the housing 10, a blower 30 for circulating air to theinside or outside of the housing 10, and an outlet 40 for dischargingair blown from the blower 30 to the outside of the housing 10.

As shown in FIG. 8, the electronic device 100 according to the thirdembodiment of the disclosure may include the microphone 110, thetransceiver 120, the processor 130, the memory 140, and a driver 160.

The microphone 110, the transceiver 120, the processor 130, and thememory 140 have been described above with reference to FIG. 2, and thedriver 160 may include an internal component of the air conditioner.Hereinafter, features of the third embodiment, which are different fromthose of the first embodiment, will be described.

The housing 10 of the electronic device 100 implemented as the airconditioner according to the third embodiment of the disclosure mayinclude, as shown in FIG. 9, a front panel 10 a in which the at leastone outlet opening 41 (41 a, 41 b, 41 c) is formed, a rear panel 10 bpositioned behind the front panel 10 a, side panels 10 c positionedbetween the front panel 10 a and the rear panel 10 b, and top and bottompanels 10 d positioned above and below the side panels 10 c. The atleast one outlet opening 41 may be in a shape of a circle, and at leasttwo or more outlet openings 41 may be provided in an up-down directionof the front panel 14 (or 10 a shown in FIG. 9) in such a way to bespaced from each other. For example, the outlet openings 41 may includea first outlet opening 41 a, a second outlet opening 41 b, and a thirdoutlet opening 41 c. A plurality of outlet holes 42 may be provided in apart of the front panel 10 a.

In each of the front panel 10 a and the side panels 10 c, a firstmicrophone 110 d may be positioned, although not limited thereto.However, the microphone 110 may be positioned in at least one of the topand bottom panels 10 d or the rear panel 10 d. Also, the microphone 110may be positioned at an appropriate location for receiving a user'sspeech.

In the rear panel 10 b, a suction port 19 may be formed to suck outsideair to the inside of the housing 10.

The suction port 19 may be positioned in the rear panel 10 b positionedbehind the heat exchanger 20 to guide outside air of the housing 10 toenter the inside of the housing 10. Air entered the inside of thehousing 10 through the suction port 19 may pass through the heatexchanger 20 to absorb or lose heat. Air exchanged heat by passingthrough the heat exchanger 20 may be discharged to the outside of thehousing 10 by the blower 30 via the outlet port 40.

The blower 30 may include a fan 162, and a grille 34 positioned in aprotruding direction of the fan 162. According to some embodiments, thefan 162 may be positioned to correspond to the at least one outletopening 41, and the number of the fan 162 is not limited. For example,the fan 162 may include a first fan 162 a, a second fan 162 b, and athird fan 162 c.

The blower 30 may include a motor 161 positioned at a center of the fan162 to drive the fan 162. For example, the motor 161 may include a firstfan motor 161 a for driving the first fan 162 a, a second fan motor 161b for driving the second fan 162 b, and a third fan motor 161 c fordriving the third fan 162 c. The motor 161 may rotate the fan 162, andthe rotating fan 162 may generate a flow of air passing through the heatexchanger 20.

The grille 34 may include a plurality of blades 35. By adjusting thenumber, shape, and installation angle of the plurality of blades 35, adirection or amount of air that is blown from the fan 162 to the outletopening 41 may be adjusted.

At a center of the grille 34, a door actuator 163 may be positioned. Thedoor actuator 163 and the motor 161 may be aligned in a front-backdirection. Through the configuration, the plurality of blades 35 of thegrille 34 may be positioned in front of fan blades of the fan 162.

The blower 30 may include a duct 36. The duct 36 may be in a shape of acircle surrounding the fan 162 to guide a flow of air flowing to the fan162.

The heat exchanger 20 may be positioned between the fan 162 and thesuction port 19 to absorb heat from air entered through the suction port19 or transfer heat to air entered through the suction port 19. The heatexchanger 20 may include a tube 21, and a header 22 coupled to upper andlower ends of the tube 21. However, a kind of the heat exchanger 20 isnot limited.

In the inside and outside of the housing 10 of the electronic device100, components for performing functions of the air conditioner, such asblowing, temperature adjustment, air purification, etc., may beprovided, and the components may be included in the driver 160.

The outlet opening 41 may be opened and closed by a door 60, and mayinclude a door blade 62 (62 a, 62 b or 62 c). The door actuator 163 mayoperate the door blade 62 to open or close the door 60. The dooractuator 163 may cause the door blade 62 to be spaced from an end 43 ofa discharge guide 45 to open the door 60, and cause the door blade 62from being in contact with the end 43 of the discharge guide 45 to closethe door 60.

The discharge guide element 45 may include a guide body 46 and a guidehole 47. The guide body 46 may form the first discharge paththereinside. The guide body 46 may be in the shape of a cylinder havinga hollow interior. More specifically, the guide body 46 may be in theshape of a pipe whose one end faces the blower unit 30 and whose otherend faces the outlet 41. The guide hole 47 may pass the second dischargepath therethrough. The guide hole 47 may be formed in the guide body 46.The shape of the guide hole 47 may be not limited, and the guide hole 47may have any structure that can be formed in the guide body 46 andenable air to flow in the outside direction of the guide body 46. In thecurrent embodiment, the guide hole 47 may be a plurality of holes formedalong the circumference of the guide body 46.

The door blade 62 may include a blade body 63 being in a shape of acircle to correspond to the outlet opening 41, and a blade couplingportion 64 extending from the blade body 63 and coupled to the dooractuator 163.

The driver 160 may include the motor 161, the fan 162, and the dooractuator 163.

In addition, the electronic device 100 may include a driving circuit forsupplying driving current to the motor 161 and a rotation sensor (forexample, an encoder, a hall sensor, etc.) for sensing rotations of thefan 162, and may include various sensors for acquiring at least one ofan operation mode, an operation intensity, an operating component, or anoperation location.

As such various components operate in different operation states,characteristics of noise generated in the electronic device 100 may alsochange accordingly.

Accordingly, the processor 130 may determine a sensitivity of a speechrecognition model capable of securing a high recognition rate and highrecognition performance according to an operation state of theelectronic device 100, and perform speech recognition based on thedetermined sensitivity. This operation has been described above in thefirst embodiment, and detailed conditions for operation states accordingto sensitivities may be modified appropriately according to animplementation example of the electronic device 100.

For example, conditions for operation states according to sensitivitiesmay be set to conditions for operation modes, and the operation modes ofthe air conditioner may be classified into a first mode which is a mildwind mode and a second mode which is a strong wind mode. In this case,the processor 130 may determine, in the first mode, a sensitivity of aspeech recognition model to be a first level, and may determine, in thesecond mode, a sensitivity of a speech recognition model to be a secondlevel having a higher sensitivity than the first level.

Also, the conditions for operation states according to sensitivities maybe set to conditions for at least one of an operation mode, an operationintensity, an operating component, or an operation location of an airconditioner. The processor 130 may identify a state condition fulfillingan operation state among predefined conditions for operation statesaccording to sensitivities, and determine a sensitivity corresponding tothe state condition fulfilling the operation state to be a sensitivityof a speech recognition model.

For this, a sensitivity table in which sensitivities for speechrecognition models match operation states may be stored in the memory140. The processor 130 may compare an operation state of the electronicdevice 100 to the sensitivity table to determine a sensitivity of aspeech recognition model corresponding to the operation state.

In this way, by determining a sensitivity of a speech recognition modelaccording to an operation state, the processor 130 may reduce influenceby noise of the electronic device 100. Accordingly, accuracy andefficiency of speech recognition may increase.

Particularly, when a speech recognition model is a wakeup wordrecognition model, a wakeup word may be accurately recognized throughsensitivity adjustment despite noise of the electronic device 100.Accordingly, the wakeup word may be recognized quickly and accuratelyalthough the user does not again utter the wakeup word.

Like the first embodiment of the disclosure, the processor 130 accordingto the third embodiment of the disclosure may change, when an operationstate changes, a sensitivity based on the changed operation state, andrecognize a user's speech based on the changed sensitivity. For this,the processor 30 may acquire an operation state in real time or atpredefined time intervals. Also, the processor 130 may change asensitivity of a speech recognition model according to a user'sintention in addition to an operation state, and generate a speechrecognition model for a noise of a surrounding environment based on theoperation state. This operation has been described above in the firstembodiment, and therefore, further descriptions thereof will be omitted.

At least one component may be added or omitted to correspond toperformance of the components of the electronic device 100 shown inFIGS. 4 and 8. Also, it will be easily understood by one of ordinaryskill in the art that relative positions of the components may change tocorrespond to the performance or structure of the system.

Meanwhile, the components shown in FIGS. 4 and 8 may be softwarecomponents and/or hardware components, such as a FPGA and an ASIC.

FIG. 11 is a flowchart showing a speech recognition method of anelectronic device according to an embodiment of the disclosure.

Referring to FIG. 11, the electronic device 100 according to anembodiment of the disclosure may determine whether an operation state isacquired, in operation 901. Herein, the operation state of theelectronic device 100 may be a state factor that may influence noisegenerated in the electronic device 100, that is, a state factor fromwhich noise of the electronic device 100 may be estimated. The operationstate may include at least one of an operation mode, an operationintensity, an operating component, or an operation location of theelectronic device 100.

After the operation state is acquired (YES in operation 901), theelectronic device 100 may select a speech recognition model based on theoperation state, in operation 902.

More specifically, the electronic device 100 may select a speechrecognition model based on the operation state from among a plurality ofspeech recognition models stored in advance in the memory 140 or aserver (not shown).

The electronic device 100 may identify a state condition fulfilling theoperation state from among predefined state conditions respectively forthe plurality of speech recognition models, and select a speechrecognition model corresponding to the state condition fulfilling theoperation state as a speech recognition model corresponding to theoperation state. At this time, a state condition for each of theplurality of speech recognition models may be defined as a condition foran operation state upon design or according to a user's input.

For this, a state condition table in which state conditions foroperation states match speech recognition models may be stored in thememory 140. The electronic device 100 may compare the acquired operationstate to the state condition table to determine a speech recognitionmodel corresponding to the operation state.

By selecting the speech recognition model corresponding to the acquiredoperation state from among the speech recognition models for theoperation states, the electronic device 100 may recognize a user'sspeech based on the selected speech recognition model, in operation 903.

As such, by selecting a speech recognition model that is suitable to anoise environment according to an operation state of the electronicdevice 100 instead of unconditionally using a speech recognition modelmodeled in a normal environment, and using the speech recognition modelfor speech recognition, a speech recognition rate and speech recognitionaccuracy may increase.

Particularly, when a speech recognition model is a wakeup wordrecognition model, a wakeup word may be accurately recognized despitenoise of the electronic device 100. Accordingly, the wakeup word may berecognized quickly and accurately although the user does not again utterthe wakeup word.

FIG. 12 is a flowchart showing a speech recognition method of anelectronic device according to an embodiment of the disclosure.

Referring to FIG. 12, the electronic device 100 according to anembodiment of the disclosure may acquire an operation state, and afterthe operation state is acquired (YES in operation 910), the electronicdevice 100 may select a speech recognition model based on the operationstate, in operation 911. Operations 910 and 911 may be the same asoperations 901 and 902 of FIG. 11.

Then, the electronic device 100 may determine whether the operationstate changes, in operation 912. The electronic device 100 may check theoperation state in real time or at predefined time intervals.

When the operation state changes (YES in operation 912), the electronicdevice 100 may change a speech recognition model to a speech recognitionmodel corresponding to the changed operation state, in operation 913.That is, the electronic device 100 may reselect a speech recognitionmodel based on the changed operation state.

The electronic device 100 may recognize a user's speech based on thechanged speech recognition model, in operation 914.

Thereby, because a speech recognition model changes according to anoperation state of the electronic device 100, a speech recognition modelthat is suitable to a noise environment according to the operation stateof the electronic device 100 may be used. Accordingly, accuracy andefficiency of speech recognition may increase.

FIG. 13 is a flowchart showing a speech recognition method of anelectronic device according to an embodiment of the disclosure.

Referring to FIG. 13, the electronic device 100 according to anembodiment of the disclosure may acquire an operation state, and afterthe electronic device 100 acquires the operation state (YES in operation921), the electronic device 100 may select a speech recognition modelbased on the operation state, in operation 922. Operations 921 and 922may be the same as operation 901 and 902 of FIG. 11.

After the electronic device 100 selects the speech recognition model,the electronic device 100 may determine a sensitivity of the speechrecognition model selected based on the operation state, in operation923. The sensitivity may be an example of a parameter of the speechrecognition model. In the current embodiment of the disclosure, thesensitivity may be provided as a parameter of the speech recognitionmodel, however, the electronic device 100 may determine various speechrecognition-related parameters based on the operation state.

More specifically, the electronic device 100 may identify a statecondition fulfilling the operation state from among predefinedconditions for operation states for sensitivities, and determine asensitivity corresponding to the state condition fulfilling theoperation state as a sensitivity of the speech recognition model. Forthis, a sensitivity table in which sensitivities of speech recognitionmodels match operation states may be stored in the memory 140. Theelectronic device 100 may compare the operation state of the electronicdevice 100 to the sensitivity table to determine a sensitivity of aspeech recognition model corresponding to the operation state.

Meanwhile, in FIG. 13, operation 923 is shown to be performed afteroperation 922 is performed, however, operations 922 and 923 may beperformed simultaneously.

The electronic device 100 may determine whether the operation statechanges, in operation 924. For this, the electronic device 100 may checkthe operation state in real time or at predefined time intervals.

When the electronic device 100 determines that the operation statechanges (YES in operation 924), the electronic device 100 may change thesensitivity based on the changed operation state, in operation 925. Morespecifically, the electronic device 100 may again determine asensitivity based on the changed operation state, thereby changing thesensitivity of the speech recognition model to a sensitivitycorresponding to the changed operation state.

Thereafter, the electronic device 100 may recognize a user's speechbased on the changed sensitivity, in operation 926.

In this way, because a sensitivity of a speech recognition model isdetermined according to an operation state and the sensitivity alsochanges according to a change of the operation state, influence by noiseof the electronic device 100 may be reduced. Accordingly, accuracy andefficiency of speech recognition may increase.

Particularly, when a speech recognition model is a wakeup wordrecognition model, a wakeup word may be accurately recognized throughsensitivity adjustment despite noise of the electronic device 100.Accordingly, the wakeup word may be recognized quickly and accuratelyalthough the user does not again utter the wakeup word.

In the electronic device and the speech recognition method thereofaccording to an aspect, because speech recognition is performed througha speech recognition model to which a surrounding noise environmentincluding noise generated in the electronic device is reflected, aspeech recognition rate and speech recognition accuracy may increase.

Meanwhile, the disclosed embodiments may be implemented in the form of arecording medium that stores instructions executable by a computer. Theinstructions may be stored in the form of program codes, and whenexecuted by a processor, the instructions may create a program module toperform operations of the disclosed embodiments. The recording mediummay be implemented as a computer-readable recording medium.

The computer-readable recording medium may include all kinds ofrecording media storing instructions that can be interpreted by acomputer. For example, the computer-readable recording medium may beROM, RAM, a magnetic tape, a magnetic disc, a flash memory, an opticaldata storage device, etc.

Although a few embodiments of the present disclosure have been shown anddescribed, it would be appreciated by those skilled in the art thatchanges may be made in these embodiments without departing from thetechnical principles and essential features of the disclosure, the scopeof which is defined in the claims and their equivalents.

While the disclosure has been shown and described with reference tovarious embodiments thereof, it will be understood by those skilled inthe art that various changes in form and details may be made thereinwithout departing from the spirit and scope of the disclosure as definedby the appended claims and their equivalents.

What is claimed is:
 1. An electronic device comprising: a microphone configured to receive a user's speech; a memory for storing a plurality of speech recognition models; and at least one processor configured to: select a speech recognition model from among the plurality of speech recognition models stored in the memory based on an operation state of the electronic device, and recognize the user's speech received by the microphone based on the selected speech recognition model.
 2. The electronic device according to claim 1, wherein the operation state includes at least one of an operation mode or an operation intensity of the electronic device.
 3. The electronic device according to claim 1, wherein the at least one processor is further configured to select a speech recognition model corresponding to a state condition fulfilling the operation state from among a plurality of state conditions respectively matching the plurality of speech recognition models, as a speech recognition model corresponding to the operation state.
 4. The electronic device according to claim 1, wherein the at least one processor is further configured to: change, when the operation state changes, the selected speech recognition model based on the changed operation state, and recognize the user's speech based on the changed speech recognition model.
 5. The electronic device according to claim 1, wherein the at least one processor is further configured to: determine a sensitivity of the selected speech recognition model based on the operation state, and recognize the user's speech based on the determined sensitivity.
 6. The electronic device according to claim 5, wherein the at least one processor is further configured to change, when the operation state changes, the sensitivity based on the changed operation state.
 7. The electronic device according to claim 5, wherein the at least one processor is further configured to: determine a user's intention based on a result of speech recognition on the user's speech, and change the sensitivity based on the user's intention.
 8. The electronic device according to claim 1, wherein the at least one processor is further configured to: determine a first pattern for noise of a surrounding environment based on the operation state, and generate a speech recognition model based on the determined first pattern.
 9. The electronic device according to claim 8, wherein the at least one processor is further configured to: determine a second pattern for noise of the electronic device based on the operation state, and determine the first pattern for the noise of the surrounding environment based on the second pattern.
 10. The electronic device according to claim 1, wherein the plurality of speech recognition models comprises a plurality of speech recognition models for recognizing a wakeup word.
 11. The electronic device according to claim 1, wherein the electronic device comprises at least one of a cleaner, an air conditioner, a refrigerator, a washing machine, or a clothes care apparatus.
 12. An electronic device comprising: a microphone configured to receive a user's speech; a transceiver configured to communicate with a server; and at least one processor configured to: select a speech recognition model from among a plurality of speech recognition models received from the server based on an operation state of the electronic device, and recognize the user's speech received by the microphone based on the selected speech recognition model.
 13. A speech recognition method of an electronic device, the speech recognition method comprising: receiving a user's speech; selecting a speech recognition model from among a plurality of speech recognition models stored in advance, based on an operation state of the electronic device; and recognizing the user's speech based on the selected speech recognition model.
 14. The speech recognition method according to claim 13, wherein the selecting of the speech recognition model comprises: selecting a speech recognition model corresponding to a state condition fulfilling the operation state from among a plurality of state conditions respectively matching the plurality of speech recognition models, as a speech recognition model corresponding to the operation state.
 15. The speech recognition method according to claim 13, further comprising: changing, when the operation state changes, the selected speech recognition model based on the changed operation state, wherein the recognizing of the user's speech comprises recognizing the user's speech based on the changed speech recognition model.
 16. The speech recognition method according to claim 13, further comprising: determining a sensitivity of the selected speech recognition model based on the operation state, wherein the recognizing of the user's speech based on the selected speech recognition model comprises recognizing the user's speech based on the determined sensitivity.
 17. The speech recognition method according to claim 16, further comprising: changing, when the operation state changes, the sensitivity of the selected speech recognition model based on the changed operation state, wherein the recognizing of the user's speech based on the selected speech recognition model comprises recognizing the user's speech based on the changed sensitivity.
 18. The speech recognition method according to claim 16, further comprising: determining a user's intention based on a result of speech recognition on the user's speech; and changing the sensitivity based on the user's intention.
 19. The speech recognition method according to claim 13, further comprising: determining a first pattern for noise of a surrounding environment based on the operation state; and generating a speech recognition model based on the determined first pattern.
 20. The speech recognition method according to claim 19, further comprising: determining a second pattern for noise of the electronic device based on the operation state, wherein the determining of the first pattern comprises determining the first pattern for the noise of the surrounding environment based on the second pattern.
 21. The speech recognition method according to claim 13, further comprising: determining the operation state based on a current location of the electronic device.
 22. The speech recognition method according to claim 13, further comprising: determining the operation state based on an operation intensity of the electronic device, wherein the operation intensity is based on a speed of operation of a component of the electronic device.
 23. The speech recognition method according to claim 22, wherein the component of the electronic device comprises at least one of a motor, a fan or a brush. 